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The optimal configuration problem of distributed generators (DGs) and shunt capacitors (SCs) in a radial distributed network (RDN) is to find the best installation locations and optimal capacities of DGs and SCs for optimizing a certain performance indicator. The discontinuity characteristic and huge computation of optimizing DGs and SCs in RDN make it no longer applicable by traditional methods. In this paper, a new bus processing method and an improved butterfly algorithm are proposed to solve the optimal configuration of DGs and SCs. The method of processing nodes considers not only the power loss index (PLI) but also the voltage amplitude of the original system, forming a sequence of candidate nodes to guide algorithm to optimizing, which can simplify the algorithm search space and improve search efficiency. Meanwhile, the butterfly algorithm with constriction factor (BF-CF) combines the inertia coefficient to introduce a constriction factor, improves the speed update model and the local search pattern, and overcomes the disadvantages of the original butterfly algorithm which is easy to fall into the local optimum and the precision of result is not high. To verify the performance of the proposed method, minimizing the active power loss and voltage deviation are selected as the objective function and reactive power loss and the worst voltage are taken as reference targets, which are performed in three standard test systems of 33-bus, 69-bus and 119-bus, respectively. The simulation results illustrate that, compared with the original butterfly algorithm and other intelligent algorithms, the method proposed in this paper has more obvious advantages and performance in solving the configuration problem of DGs and SCs in systems of various scales. [ABSTRACT FROM AUTHOR]

Distributed generators (DGs) are recognized as an effective method for controlling the power loss, voltage stability, etc.. In this paper, a novel hybrid algorithm of beetle antennae search (BAS) and particle swarm optimization (PSO) is presented for optimal allocation of DGs in radial distribution network. The BAS describes the beetle's individual search by smell, PSO describes the group search of birds by location. The proposed algorithm combines their advantages, proceeds with individual optimization while conducting group optimization. Therefore, the proposed algorithm searches widely, and converges fast. In this paper, a series of improvement measures are proposed to deal with the shortcoming of PSO-BAS which is easy to fall into local optimum. These methods include equal interval initialization, cross mutation, and non-linear learning factor. This paper will show the comparison results of PSO-BAS and IPSO-BAS in the six confessed test functions to prove the necessity of the improved methods. Simultaneously, in order to verify the feasibility and effectiveness of this proposed algorithm in terms of practical application, it is tested on the standard IEEE 33-bus, IEEE 69-bus and IEEE 119-bus systems. The results of active power loss and voltage stability show that proposed algorithm is more effective and more suitable for the power distribution system than other algorithms. At the same time, this article also explores the impact of new energy sources on the annual network loss. Besides, a method for optimizing the annual network loss is proposed. Here, the IPSO-BAS algorithm is used to adjust the size of multiple biomass energy sources within 24 hours to optimize the network loss, and a comparison plan is designed to verify the feasibility of the proposed method. According to the final result, this proposed method can greatly reduce the annual network loss. [ABSTRACT FROM AUTHOR]